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Stochastic Extended LQR for Optimization-based Motion Planning Under Uncertainty.

Wen Sun1, Jur van den Berg2, Ron Alterovitz3

  • 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA.

IEEE Transactions on Automation Science and Engineering : a Publication of the IEEE Robotics and Automation Society
|February 7, 2017
PubMed
Summary

We developed Stochastic Extended LQR (SELQR), a new motion planner for robots with uncertain movements. SELQR efficiently finds optimal paths by minimizing expected costs, improving robotic navigation and control.

Keywords:
motion planning under uncertaintynonholonomic motion planning

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Area of Science:

  • Robotics
  • Control Theory
  • Optimization

Background:

  • Robotic systems often face challenges with non-linear dynamics and motion uncertainty.
  • Accurate trajectory planning is crucial for safe and efficient robotic operations.

Purpose of the Study:

  • Introduce Stochastic Extended LQR (SELQR), an optimization-based motion planner.
  • Develop a method to minimize expected costs for robotic systems with stochastic dynamics.

Main Methods:

  • SELQR utilizes forward and backward value iteration to estimate costs.
  • Iterative local optimization refines trajectories and control policies.
  • The planner handles state- and control-dependent Gaussian motion uncertainty.

Main Results:

  • SELQR demonstrates fast and reliable convergence to high-quality plans.
  • The approach yields smoothed states for improved dynamics linearization and cost function approximation.
  • Successfully applied to simulated car-like robots, quadrotors, and medical steerable needles.

Conclusions:

  • SELQR provides an effective solution for motion planning in stochastic, non-linear robotic systems.
  • The planner's ability to operate in belief space enhances its utility for systems with imperfect sensing.
  • SELQR offers a robust and efficient method for generating optimal trajectories and control policies.